R Under development (unstable) (2024-07-10 r86888 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(fitdistrplus)
Loading required package: MASS
Loading required package: survival
> 
> # ?cdfcomp
> visualize <- FALSE # TRUE for manual tests with visualization of results
> nsample <- 1000
> nsample <- 10
> 
> 
> # (1) Plot various distributions fitted to serving size data
> #
> data(groundbeef)
> serving <- groundbeef$serving
> fitW <- fitdist(serving,"weibull")
> fitln <- fitdist(serving,"lnorm")
> fitg <- fitdist(serving,"gamma")
> 
> #sanity checks
> try(cdfcomp("list(fitW, fitln, fitg)",horizontals = FALSE), silent=TRUE)
> try(cdfcomp(list(fitW, fitln, fitg, a=1),horizontals = FALSE), silent=TRUE)
> 
> #real call
> res <- cdfcomp(list(fitW, fitln, fitg), horizontals = FALSE)
> str(res)
List of 2
 $ obs          : num [1:254, 1:2] 10 11.5 17 20 20 20 20 20 20 20 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : NULL
  .. ..$ : chr [1:2] "sdata" "obsp"
 $ probabilities: num [1:101, 1:3] 0.00966 0.0141 0.01943 0.02568 0.03284 ...
> 
> cdfcomp(list(fitW, fitln, fitg), horizontals = TRUE)
> cdfcomp(list(fitW, fitln, fitg), horizontals = TRUE, lines01 = TRUE)
> cdfcomp(list(fitW, fitln, fitg), horizontals = TRUE, verticals = TRUE, datacol = "grey")
> if (requireNamespace ("ggplot2", quietly = TRUE)) {
+   cdfcomp(list(fitW, fitln, fitg), horizontals = FALSE, plotstyle = "ggplot")
+ }
> if (requireNamespace ("ggplot2", quietly = TRUE) & visualize) {
+   cdfcomp(list(fitW, fitln, fitg), horizontals = TRUE, plotstyle = "ggplot")
+   cdfcomp(list(fitW, fitln, fitg), horizontals = TRUE, lines01 = TRUE, plotstyle = "ggplot")
+   cdfcomp(list(fitW, fitln, fitg), horizontals = TRUE, verticals = TRUE, datacol = "grey", plotstyle = "ggplot")
+ }
> 
> cdfcomp(list(fitW, fitln, fitg), legendtext = c("Weibull", "lognormal", "gamma"),
+         main = "ground beef fits", xlab = "serving sizes (g)",
+         ylab = "F(g)", xlim = c(0, 250), ylim = c(.5, 1))
> if (requireNamespace ("ggplot2", quietly = TRUE) & visualize) {
+   cdfcomp(list(fitW, fitln, fitg), legendtext = c("Weibull", "lognormal", "gamma"),
+           main = "ground beef fits", xlab = "serving sizes (g)",
+           ylab = "F(g)", xlim = c(0, 250), ylim = c(.5, 1), plotstyle = "ggplot")
+ }
> 
> cdfcomp(list(fitW, fitln, fitg), legendtext = c("Weibull", "lognormal", "gamma"),
+         main = "ground beef fits", xlab = "serving sizes (g)",
+         ylab = "F(g)", xlogscale = TRUE)
> if (requireNamespace ("ggplot2", quietly = TRUE) & visualize) {
+   cdfcomp(list(fitW, fitln, fitg), legendtext = c("Weibull", "lognormal", "gamma"),
+           main = "ground beef fits", xlab = "serving sizes (g)",
+           ylab = "F(g)", xlogscale = TRUE, plotstyle = "ggplot")
+ }
> 
> cdfcomp(list(fitW,fitln,fitg),legendtext=c("Weibull","lognormal","gamma"),
+         main="ground beef fits",xlab="serving sizes (g)",
+         ylab="F(g)", xlogscale=TRUE, ylogscale=TRUE, ylim=c(.005, .99))
> if (requireNamespace ("ggplot2", quietly = TRUE) & visualize) {
+   cdfcomp(list(fitW,fitln,fitg),legendtext=c("Weibull","lognormal","gamma"),
+           main="ground beef fits",xlab="serving sizes (g)",
+           ylab="F(g)", xlogscale=TRUE, ylogscale=TRUE, ylim=c(.005, .99), plotstyle = "ggplot")
+ }
> 
> cdfcomp(list(fitW,fitln,fitg), legendtext=c("Weibull","lognormal","gamma"),
+         main="ground beef fits",xlab="serving sizes (g)",
+         ylab="F(g)",xlim = c(0,250), xlegend = "topleft")
> if (requireNamespace ("ggplot2", quietly = TRUE) & visualize) {
+   cdfcomp(list(fitW,fitln,fitg), legendtext=c("Weibull","lognormal","gamma"),
+           main="ground beef fits",xlab="serving sizes (g)",
+           ylab="F(g)",xlim = c(0,250), xlegend = "topleft", plotstyle = "ggplot")
+ }
> 
> 
> 
> 
> # (2) Plot lognormal distributions fitted by 
> # maximum goodness-of-fit estimation
> # using various distances (data plotted in log scale)
> #
> data(endosulfan)
> ATV <-subset(endosulfan, group == "NonArthroInvert")$ATV
> taxaATV <- subset(endosulfan, group == "NonArthroInvert")$taxa
> flnMGEKS <- fitdist(ATV,"lnorm",method="mge",gof="KS")
> flnMGEAD <- fitdist(ATV,"lnorm",method="mge",gof="AD")
> flnMGEADL <- fitdist(ATV,"lnorm",method="mge",gof="ADL")
> flnMGEAD2L <- fitdist(ATV,"lnorm",method="mge",gof="AD2L")
> llfit <- list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L)
> 
> cdfcomp(list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L), xlogscale=TRUE, 
+         main="fits of a lognormal dist. using various GOF dist.")
> cdfcomp(list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L),
+         xlogscale=TRUE,main="fits of a lognormal dist. using various GOF dist.",
+         legendtext=c("MGE KS","MGE AD","MGE ADL","MGE AD2L"))
> cdfcomp(list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L),
+         xlogscale=TRUE,main="fits of a lognormal dist. using various GOF dist.",
+         legendtext=c("MGE KS","MGE AD","MGE ADL","MGE AD2L"), 
+         fitcol=c("black", "darkgreen", "yellowgreen", "yellow2"), 
+         horizontals=FALSE, datapch="+")
> cdfcomp(llfit, xlogscale=TRUE, main="fits of a lognormal dist. using various GOF dist.",
+         legendtext=paste("MGE", c("KS","AD","ADL","AD2L")), fitcol="grey35", 	
+         fitlty="dotted", horizontals=FALSE, datapch=21, datacol="grey30")
> cdfcomp(list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L),
+         xlogscale=TRUE, verticals=TRUE, xlim=c(10,100000), datapch=21)
> cdfcomp(flnMGEKS, xlogscale=TRUE, verticals=TRUE, xlim=c(10,100000))
> cdfcomp(list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L),
+         xlogscale=TRUE, verticals=TRUE, xlim=c(1,100000), datapch=21, name.points=taxaATV)
> cdfcomp(flnMGEKS, xlogscale=TRUE, verticals=TRUE, xlim=c(1,100000), name.points=taxaATV)
> 
> if (requireNamespace ("ggplot2", quietly = TRUE) & visualize) {
+   cdfcomp(list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L), xlogscale=TRUE,
+           main = "fits of a lognormal dist. using various GOF dist.", plotstyle = "ggplot")
+   cdfcomp(list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L),
+           xlogscale=TRUE,main="fits of a lognormal dist. using various GOF dist.",
+           legendtext=c("MGE KS","MGE AD","MGE ADL","MGE AD2L"), plotstyle = "ggplot")
+   cdfcomp(list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L),
+           xlogscale=TRUE,main="fits of a lognormal dist. using various GOF dist.",
+           legendtext=c("MGE KS","MGE AD","MGE ADL","MGE AD2L"), 
+           fitcol=c("black", "darkgreen", "yellowgreen", "yellow2"), 
+           horizontals=FALSE, datapch="+", plotstyle = "ggplot")
+   cdfcomp(llfit, xlogscale=TRUE, main="fits of a lognormal dist. using various GOF dist.",
+           legendtext=paste("MGE", c("KS","AD","ADL","AD2L")), fitcol="grey35", 	
+           fitlty="dotted", horizontals=FALSE, datapch=21, datacol="grey30", plotstyle = "ggplot")
+   cdfcomp(list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L),
+           xlogscale=TRUE, verticals=TRUE, xlim=c(10,100000), datapch=21, plotstyle = "ggplot")
+   cdfcomp(flnMGEKS, xlogscale=TRUE, verticals=TRUE, xlim=c(10,100000), plotstyle = "ggplot")
+   cdfcomp(list(flnMGEKS, flnMGEAD, flnMGEADL, flnMGEAD2L),
+           xlogscale=TRUE, verticals=TRUE, xlim=c(1,100000), datapch=21, name.points=taxaATV, 
+           plotstyle = "ggplot")
+   cdfcomp(flnMGEKS, xlogscale=TRUE, verticals=TRUE, xlim=c(1,100000), name.points=taxaATV, 
+           plotstyle = "ggplot")
+ }
> 
> 
> 
> 
> # (3) Plot normal and logistic distributions fitted by 
> # maximum likelihood estimation
> # using various plotting positions in cdf plots
> #
> if (visualize)
+ {
+   data(endosulfan)
+   log10ATV <-log10(subset(endosulfan, group == "NonArthroInvert")$ATV)
+   taxaATV <- subset(endosulfan, group == "NonArthroInvert")$taxa
+   fln <- fitdist(log10ATV, "norm")
+   fll <- fitdist(log10ATV, "logis")
+   
+   # default plot using Hazen plotting position: (1:n - 0.5)/n
+   cdfcomp(list(fln, fll), legendtext = c("normal", "logistic"), xlab = "log10ATV", 
+           name.points=taxaATV, xlim = c(0,5))
+   if (requireNamespace ("ggplot2", quietly = TRUE)) {
+     cdfcomp(list(fln, fll), legendtext = c("normal", "logistic"),xlab = "log10ATV", 
+             name.points=taxaATV, xlim=c(0,5), plotstyle = "ggplot")
+   }
+   
+   # plot using mean plotting position (named also Gumbel plotting position)
+   # (1:n)/(n + 1)
+   cdfcomp(list(fln,fll),legendtext=c("normal","logistic"),xlab="log10ATV",
+           use.ppoints = TRUE, a.ppoints = 0)
+   if (requireNamespace ("ggplot2", quietly = TRUE)) {
+     cdfcomp(list(fln,fll),legendtext=c("normal","logistic"),xlab="log10ATV",
+             use.ppoints = TRUE, a.ppoints = 0, plotstyle = "ggplot")
+   }
+   
+   # plot using basic plotting position: (1:n)/n
+   cdfcomp(list(fln,fll),legendtext=c("normal","logistic"),xlab="log10ATV",
+           use.ppoints = FALSE)
+   if (requireNamespace ("ggplot2", quietly = TRUE)) {
+     cdfcomp(list(fln,fll),legendtext=c("normal","logistic"),xlab="log10ATV",
+             use.ppoints = FALSE, plotstyle = "ggplot")
+   }
+ }
> 
> 
> 
> 
> # (4) Plot lognormal distributions fitted by 
> # maximum goodness-of-fit estimation
> # using various distances (data plotted in log scale)
> #
> if (visualize)
+ {
+   x1 <- c(6.4,13.3,4.1,1.3,14.1,10.6,9.9,9.6,15.3,22.1,13.4,13.2,8.4,6.3,8.9,5.2,10.9,14.4)
+   x <- seq(0, 1.1*max(x1), length=100)
+   
+   dgumbel <- function(x,a,b) 1/b*exp((a-x)/b)*exp(-exp((a-x)/b))
+   pgumbel <- function(q,a,b) exp(-exp((a-q)/b))
+   
+   f1 <- mledist(x1, "norm")
+   f2 <- mledist(x1, "gumbel", start = list(a = 10, b = 5))
+   f3 <- mledist(x1, "exp")
+   
+   # graph 1
+   plot(ecdf(x1))
+   lines(x, pnorm(x, f1$estimate[1], f1$estimate[2]), col = "red")
+   lines(x, pgumbel(x, f2$estimate[1], f2$estimate[2]), col = "green")
+   lines(x, pexp(x, f3$estimate[1]), col = "blue")
+   legend("bottomright", lty = 1, leg = c("Normal", "Gumbel", "Exp"), col = c("red", "green", "blue"))
+   
+   # graph 2
+   f1 <- fitdist(x1, "norm")
+   f2 <- fitdist(x1, "gumbel", start = list(a = 10, b = 5))
+   f3 <- fitdist(x1, "exp")
+   cdfcomp(list(f1, f2, f3), xlim=range(x), fitcol = c("red", "green", "blue"), fitlty = 1, legendtext = c("Normal", "Gumbel", "Exp"))
+   cdfcomp(list(f1, f2, f3), xlim=range(x), fitcol = c("red", "green", "blue"), fitlty = 1, fitlwd = (1:3)*2, legendtext = c("Normal", "Gumbel", "Exp"))
+   
+   # graph 3
+   if (requireNamespace ("ggplot2", quietly = TRUE)) {
+     cdfcomp(list(f1, f2, f3), xlim=range(x), fitcol=c("red","green","blue"), fitlty = 1, legendtext = c("Normal", "Gumbel", "Exp"), plotstyle = "ggplot")
+     cdfcomp(list(f1, f2, f3), xlim=range(x), fitcol=c("red","green","blue"), fitlty = 1, fitlwd = 1:3, legendtext = c("Normal", "Gumbel", "Exp"), plotstyle = "ggplot")
+   }
+ }
> 
> # (5) normal mixture
> #
> 
> # mixture of two normal distributions
> # density
> dnorm2 <- function(x, poid, m1, s1, m2, s2)
+   poid*dnorm(x, m1, s1) + (1-poid)*dnorm(x, m2, s2)
> # numerically approximate quantile function
> qnorm2 <- function(p, poid, m1, s1, m2, s2)
+ {
+   L2 <- function(x, prob)
+     (prob - pnorm2(x, poid, m1, s1, m2, s2))^2	
+   sapply(p, function(pr) optimize(L2, c(-1000, 1000), prob=pr)$minimum)
+ }	
> # distribution function		
> pnorm2 <- function(q, poid, m1, s1, m2, s2)
+   poid*pnorm(q, m1, s1) + (1-poid)*pnorm(q, m2, s2)		
> 
> 
> # basic normal distribution
> set.seed(1234)
> x2 <- c(rnorm(nsample, 5),  rnorm(nsample, 10))
> # MLE fit
> fit1 <- fitdist(x2, "norm2", "mle", start=list(poid=1/3, m1=4, s1=2, m2=8, s2=2), 
+                 lower=c(0, 0, 0, 0, 0))
Warning messages:
1: In cov2cor(varcovar) :
  diag(V) had non-positive or NA entries; the non-finite result may be dubious
2: In sqrt(diag(varcovar)) : NaNs produced
> fit2 <- fitdist(x2, "norm2", "qme", probs=c(1/6, 1/4, 1/3, 1/2, 2/3), 
+                 start=list(poid=1/3, m1=4, s1=2, m2=8, s2=2), 
+                 lower=c(0, 0, 0, 0, 0), upper=c(1/2, Inf, Inf, Inf, Inf))
> fit3 <- fitdist(x2, "norm2", "mge", gof="AD", 
+                 start=list(poid=1/3, m1=4, s1=2, m2=8, s2=2), 
+                 lower=c(0, 0, 0, 0, 0), upper=c(1/2, Inf, Inf, Inf, Inf))
> 
> cdfcomp(list(fit1, fit2, fit3), datapch=".")
> cdfcomp(list(fit1, fit2, fit3), datapch=".", xlim=c(6, 8), ylim=c(.4, .55))
> 
> if (requireNamespace ("ggplot2", quietly = TRUE) & visualize) {
+   cdfcomp(list(fit1, fit2, fit3), datapch=".", plotstyle = "ggplot")
+   cdfcomp(list(fit1, fit2, fit3), datapch=".", xlim=c(6, 8), ylim=c(.4, .55), plotstyle = "ggplot")
+ }
> 
> 
> 
> 
> # (6) discrete example
> #
> set.seed(1234)
> x3 <- rpois(20, 10)
> 
> fit1 <- fitdist(x3, "pois", "mle")
> fit2 <- fitdist(x3, "nbinom", "qme", probs=c(1/3, 2/3))
> 
> cdfcomp(list(fit1, fit2), datapch=21, horizontals=FALSE)
> if (requireNamespace ("ggplot2", quietly = TRUE) & visualize) {
+   cdfcomp(list(fit1, fit2), datapch=21, horizontals=FALSE, plotstyle = "ggplot")
+ }
> 
> 
> # (7) large dataset
> #
> if (visualize)
+ {
+   n <- 1e4
+   f1 <- fitdist(rlnorm(n), "lnorm")
+   
+   cdfcomp(f1, do.points=TRUE)
+   cdfcomp(f1, do.points=FALSE)
+   cdfcomp(f1, horizontals = FALSE, verticals = FALSE, do.points = FALSE)
+   if (requireNamespace ("ggplot2", quietly = TRUE) & visualize) {
+     cdfcomp(f1, do.points = TRUE, plotstyle = "ggplot")
+     cdfcomp(f1, do.points = FALSE, plotstyle = "ggplot")
+     cdfcomp(f1, horizontals = FALSE, verticals = FALSE, do.points = FALSE, plotstyle = "ggplot")
+   }
+   
+ }
> 
> # (8) argument add (must give the same plot (except colors) as ex. 6)
> #
> set.seed(1234)
> x3 <- rpois(nsample, 10)
> 
> fit1 <- fitdist(x3, "pois", "mle")
> cdfcomp(fit1, fitcol = "red", horizontals=FALSE, addlegend = FALSE)
> fit2 <- fitdist(x3, "nbinom", "qme", probs=c(1/3, 2/3))
> cdfcomp(fit2, fitcol = "blue", horizontals=FALSE, addlegend = FALSE, add = TRUE)
> cdfcomp(list(fit1, fit2), horizontals=FALSE, addlegend = FALSE, 
+         fitcol=c("red", "blue"))
> 
> 
> if (requireNamespace ("ggplot2", quietly = TRUE) & visualize) {
+   # the argument add is not available when plotstyle = "ggplot"
+   cdfcomp(list(fit1, fit2), fitcol = c("red", "blue"), fitlty = 1, horizontals = FALSE, addlegend = FALSE, plotstyle = "ggplot")
+ }
> 
> 
> # (9) test legend labels
> #
> if (visualize)
+ {
+   serving <- groundbeef$serving
+   fitW <- fitdist(serving,"weibull")
+   fitW2 <- fitdist(serving,"weibull", method="qme", probs=c(1/3,2/3))
+   fitW3 <- fitdist(serving,"weibull", method="qme", probs=c(1/2,2/3))
+   fitln <- fitdist(serving,"lnorm")
+   fitg <- fitdist(serving,"gamma")
+   
+   cdfcomp(list(fitW, fitln, fitg)) #distrib
+   cdfcomp(list(fitW, fitW2, fitln, fitg)) #distrib+method
+   cdfcomp(list(fitW, fitW2, fitW3, fitln, fitg)) #distrib+method+num
+   if (requireNamespace ("ggplot2", quietly = TRUE))
+     cdfcomp(list(fitW, fitW2, fitW3, fitln, fitg), plotstyle = "ggplot") #distrib+method+num
+   
+ }
> 
> proc.time()
   user  system elapsed 
   3.09    0.32    3.40